1. Technical Field
The present disclosure relates to resonator(s).
2. Discussion of Related Art
Some forms of signal processing, such as pattern recognition, data mining and sensor signal processing, involve classifying or categorizing data.
Classifying and categorizing data has been the subject of intensive research for several decades. For example, an audio pattern recognition based on resonators is described on page 146 of “Self-Organisation and Associative Memory” by Teuvo Kohonen (Springer, 1984).
It has been proposed to implement pattern recognition in hardware.
In recent years, machine learning algorithms have evolved for classifying data. These algorithms tend to use digital signal processors and employ mathematical methods based on statistical methods and optimization processes.
An example of classifying data will now be described.
A chemical sensor system or “artificial nose” can be used to identify an odor by measuring concentrations of n different chemicals. The result of a measurement is an n-dimensional vector of measurement values. Recognizing a particular odor involves determining if the n-dimensional vector belongs to a specific cluster of points in n-dimensional space. The system learns to classify these points using certain mathematical rules known as “discriminant functions” which divide n-dimensional space into decision regions.
FIG. 1 illustrates a simple example of a two-dimensional space 1 in which data values 2 are classified into three groups 3 by three discriminant functions 4. Discrimination can be carried out based on a method using a form of discriminant known as a support vector machine (SVM). A discriminant, g(x), is defined in terms of a set of support vectors, αt, and a non-linear Kernel function K(xt,x), namely:
                              g          ⁡                      (            x            )                          =                              ∑            t                    ⁢                                          ⁢                                    α              t                        ⁢                          r              t                        ⁢                          K              ⁡                              (                                                      x                    t                                    ,                  x                                )                                                                        (        1        )            and where, in this case, a Gaussian radial basis Kernel function K(xt,x) is used, namely:
                              K          ⁡                      (                                          x                t                            ,              x                        )                          =                  exp          ⁡                      [                          -                                                                                                                                      x                        t                                            -                      x                                                                            2                                                  σ                  2                                                      ]                                              (        2        )            
The Kernel function is typically calculated using a digital signal processor using a multiplication unit.
It may be useful for portable devices, e.g. handheld devices or smaller-sized devices, to classify or categorize data. However, these types of devices may have limited-capacity power sources and/or limited computing resources.